“Balancing Expectations with Actual Realities”: Conversations with Clinicians and Scientists in the First Year of a High-Risk Childhood Cancer Precision Medicine Trial
Bibliographic record
Abstract
= 15) experiences in the first year of the PRecISion Medicine for Children with Cancer (PRISM) trial for children and adolescents with high-risk cancers, through an in-depth semi-structured interview. We thematically analysed participants' responses regarding their professional challenges, and measured oncologists' knowledge of genetics and confidence with somatic and germline molecular test results. Both groups described positive early experiences with PRISM but were cognisant of managing parents' expectations. Key challenges for clinicians included understanding and communicating genomic results, balancing biopsy risks, and drug access. Most oncologists rated 'good' knowledge of genetics, but a minority were 'very confident' in interpreting (25%), explaining (34.4%) and making treatment recommendations (18.8%) based on somatic genetic test results. Challenges for scientists included greater emotional impact of their work and balancing translational outputs with academic productivity. Continued tracking of these challenges across the course of the trial, while assessing the perspectives of a wider range of stakeholders, is critical to drive the ongoing development of a workforce equipped to manage the demands of paediatric precision medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".